package com.ymf.aiagent.app;

import com.ymf.aiagent.advisor.MyLoggerAdvisor;
import com.ymf.aiagent.chatMemory.FileBasedChatMemory;
import com.ymf.aiagent.rag.LoveAppRagCustomAdvisorFactory;
import com.ymf.aiagent.rag.QueryRewriter;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.client.advisor.api.Advisor;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.tool.ToolCallback;
import org.springframework.ai.tool.ToolCallbackProvider;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Component;
import reactor.core.publisher.Flux;

import java.util.List;
import java.util.Optional;

import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;

@Slf4j
@Component
public class LoveApp {
    private final ChatClient chatClient;

    private static final String SYSTEM_PROMPT = "扮演深耕恋爱心理领域的专家。开场向用户表明身份，告知用户可倾诉恋爱难题。" +
            "围绕单身、恋爱、已婚三种状态提问：单身状态询问社交圈拓展及追求心仪对象的困扰；" +
            "恋爱状态询问沟通、习惯差异引发的矛盾；已婚状态询问家庭责任与亲属关系处理的问题。" +
            "引导用户详述事情经过、对方反应及自身想法，以便给出专属解决方案。";
    public LoveApp(ChatModel dashScopeChatModel) {
            //初始化基于内存的聊天记录
//        ChatMemory chatMemory = new InMemoryChatMemory();
        //初始化基于文件内存的对话记忆
        String fileDir = System.getProperty("user.dir") + "/tmp/chat-memory";
        ChatMemory chatMemory = new FileBasedChatMemory(fileDir);
        chatClient = ChatClient
                    .builder(dashScopeChatModel)
                    .defaultSystem(SYSTEM_PROMPT)
                    .defaultAdvisors(
                            new MessageChatMemoryAdvisor(chatMemory),
                            new MyLoggerAdvisor()
                    )
                    .build();
    }


    /**
     * AI 基础对话（支持多轮对话记忆）
     *
     * @param message
     * @param chatId
     * @return
     */
    public String doChat(String message,String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId).param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }

    /**
     * AI 基础对话（支持多轮对话记忆，SSE 流式传输）
     *
     * @param message
     * @param chatId
     * @return
     */
    public Flux<String> doChatByStream(String message, String chatId) {
        return chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId))
                .stream()
                .content();
    }


    // jdk14声明类 ： 恋爱报告功能实体类结构化
    record LoveReport (String title, List<String> suggestions){}
    /**
     * AI 恋爱报告功能（实战结构化输出）
     *
     * @param message
     * @param chatId
     * @return
     */
    public LoveReport doChatWithReport(String message, String chatId) {
        LoveReport loveReport = chatClient
                .prompt()
                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId))
                .call()
                .entity(LoveReport.class);
        log.info("loveReport: {}", loveReport);
        return loveReport;

    }

    @Resource
    private VectorStore loveAppVectorStore;

    @Resource
    private Advisor loveAppRagCloudAdvisor;

//    @Resource
//    private VectorStore pgVectorVectorStore;

    @Resource
    private QueryRewriter queryRewriter;
    /**
     * 使用Rag 知识库增加进行对话
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithRag(String message, String chatId) {

        // 查询重写 (查询重写、扩展、翻译、整合去重都是提交给Ai进行操作的因此会额外消耗token)
        String rewrittenMessage = queryRewriter.doQueryRewrite(message);

        ChatResponse chatResponse = chatClient
                .prompt()
                .user(rewrittenMessage)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                // 开启日志，便于观察效果
                .advisors(new MyLoggerAdvisor())
//                 应用本地知识库问答 查询增强QuestionAnswerAdvisor
//                .advisors(new QuestionAnswerAdvisor(loveAppVectorStore))
                //应用增强检索服务(云知识库服务) RetrievalAugmentationAdvisor
//                .advisors(loveAppRagCloudAdvisor)
                // 应用 RAG 检索增强服务（基于 PgVector 向量存储）
//                .advisors(new QuestionAnswerAdvisor(pgVectorVectorStore))
                // 应用自定义的 RAG 检索增强服务（文档查询器 + 上下文增强器）
                .advisors(LoveAppRagCustomAdvisorFactory.createLoveAppRagCustomAdvisor(loveAppVectorStore, "单身"))
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }

    //Ai 调用工具能力
    @Resource
    private ToolCallback allTools;

    /**
     * AI 恋爱报告功能（支持调用工具）
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithTools(String message, String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId))
                // 开启日志，便于观察效果
                .advisors(new MyLoggerAdvisor())
                .tools(allTools)
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }

    //Ai 调用mcp服务能力
//    @Resource
//    private ToolCallbackProvider toolCallbackProvider;

    /**
     * AI 恋爱报告功能（支持调用工具）
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithMcp(String message, String chatId) {
//        ChatResponse chatResponse = chatClient
//                .prompt()
//                .user(message)
//                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId))
//                // 开启日志，便于观察效果
//                .advisors(new MyLoggerAdvisor())
//                .tools(toolCallbackProvider)
//                .call()
//                .chatResponse();
//        String content = chatResponse.getResult().getOutput().getText();
//        log.info("content: {}", content);
        String content = "mcp服务未启用";
        return content;
    }



}
